Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management

合作研究：URoL:ASC：应用生命规则预测浮游植物的紧急行为并推进水质管理

• 批准号: 2318862
• 负责人: Renato Figueiredo
• 金额: $ 15.47万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318862&HistoricalAwards=false
• 关键词: Collaborative; Research; URoL; ASC; Applying

Drinking water safety is threatened globally by increasing phytoplankton blooms in lakes and reservoirs, which pose major threats to water quality via harmful toxins, scums, and changes in taste and odor. To improve drinking water management in the face of global change, this project proposes to develop the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty in water quality predictions. If managers had forecasts of phytoplankton blooms, they could preemptively act to mitigate water quality impairment, such as by adapting water treatment, thereby decreasing costs and improving drinking water safety. The project team plans to integrate cutting-edge lake ecosystem and statistical modeling with new computing capacity to deliver 1 to 35 day-ahead forecasts of phytoplankton blooms to water managers daily for several U.S. lakes. Researchers intend to work with water managers on the forecasting system to generate valuable knowledge about how best to effectively communicate forecasts for improved water resource decision-making. The project team also plans to develop teaching modules on forecasting and freshwater ecosystems for high school students and community college students in water management/wastewater certificate programs, thereby improving both water quality and water worker training in central Appalachia. The teaching modules will be made available to colleges and universities across the U.S. as part of an existing educational program that has reached over 100,000 students to date.Phytoplankton blooms in lakes are a type of emergent behavior that can have ecosystem-scale, societally important consequences by degrading water quality, yet are challenging to predict. A fundamental Rule of Life governs this behavior: ecosystem-scale emergence is a function of environmental dynamics operating on individual organisms (e.g., temperature and light effects on phytoplankton growth rates), mediated by population and community processes (e.g., multi-species interactions that promote increased phytoplankton biomass). This project will apply a Rules of Life approach to solve a major societal problem by implementing emergent phytoplankton behavior into predictive models to generate real-time lake water quality forecasts with cloud and edge computing tools. This research is uniquely enabled by a transdisciplinary team with expertise that spans the biological sciences, social and decision sciences, physical sciences, computer and data sciences, and statistics, as well as long-term partnerships with managers, educators, and community members. Advances from this convergent, use-inspired research approach will include: 1) improved understanding of how a Rule of Life can be used to predict emergent, ecosystem-scale phenomena; 2) new cyberinfrastructure for transferring data from environmental sensors to the cloud; 3) generation of novel, computationally-tractable statistical methods for real-time forecasting with individual-based models; 4) greater understanding of how water management and ecosystem dynamics interact to control phytoplankton; 5) creation of new tools that effectively communicate forecast uncertainty; and 6) capacity-building by providing innovative training for researchers, managers, and students that broadens STEM participation across central Appalachia. Through novel, cross-disciplinary integration, this project aims to develop a forecasting system that will become a model for drinking water systems in communities globally.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

全球范围内的饮用水安全受到湖泊和水库中浮游植物大量繁殖的威胁，这些浮游植物通过有害毒素、浮渣以及味道和气味的变化对水质构成重大威胁。为了在全球变化的情况下改善饮用水管理，该项目建议开发第一个自动化、实时的湖泊浮游植物预测系统，该系统可以量化水质预测的不确定性。如果管理者能够预测浮游植物大量繁殖，他们就可以先发制人地采取行动，减轻水质损害，例如通过调整水处理，从而降低成本并提高饮用水安全。该项目团队计划将尖端的湖泊生态系统和统计模型与新的计算能力相结合，每天向美国多个湖泊的水管理人员提供浮游植物繁殖的提前 1 至 35 天的预测。研究人员打算与水资源管理者合作开发预测系统，以获取有关如何最好地有效传达预测结果以改进水资源决策的宝贵知识。 该项目团队还计划为高中生和社区学院的水管理/废水证书课程的学生开发预测和淡水生态系统的教学模块，从而改善阿巴拉契亚中部的水质和水务工人培训。 这些教学模块将作为现有教育计划的一部分向美国各地的学院和大学提供，该计划迄今为止已覆盖超过 100,000 名学生。湖泊中的浮游植物大量繁殖是一种突发行为，可能会产生生态系统规模的社会重要后果水质恶化，但难以预测。生命的基本规则控制着这种行为：生态系统规模的出现是对个体生物体起作用的环境动态的函数（例如，温度和光对浮游植物生长速率的影响），由种群和群落过程（例如，多物种相互作用）介导。促进浮游植物生物量的增加）。该项目将应用生命规则方法来解决重大社会问题，将浮游植物的浮游植物行为纳入预测模型，利用云和边缘计算工具生成实时湖泊水质预测。这项研究是由一个跨学科团队独特地促成的，该团队的专业知识涵盖生物科学、社会和决策科学、物理科学、计算机和数据科学以及统计学，以及与管理者、教育工作者和社区成员的长期合作伙伴关系。这种融合的、受使用启发的研究方法所取得的进展将包括：1）更好地理解如何使用生命规则来预测生态系统规模的新兴现象； 2）新的网络基础设施，用于将数据从环境传感器传输到云端； 3）生成新颖的、易于计算处理的统计方法，用于基于个体的模型进行实时预测； 4）更好地了解水管理和生态系统动态如何相互作用以控制浮游植物； 5）创建有效传达预测不确定性的新工具； 6) 通过为研究人员、管理人员和学生提供创新培训来进行能力建设，扩大阿巴拉契亚中部地区对 STEM 的参与。通过新颖的跨学科整合，该项目旨在开发一个预测系统，该系统将成为全球社区饮用水系统的典范。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和知识进行评估，被认为值得支持。更广泛的影响审查标准。